{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import scorecardpipeline as sp\n",
    "from scorecardpipeline import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'scorecardpipeline' from '../scorecardpipeline/__init__.py'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = sp.init_setting(seed=6666, logger=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status_of_existing_checking_account</th>\n",
       "      <th>duration_in_month</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings_account_and_bonds</th>\n",
       "      <th>present_employment_since</th>\n",
       "      <th>installment_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>personal_status_and_sex</th>\n",
       "      <th>other_debtors_or_guarantors</th>\n",
       "      <th>...</th>\n",
       "      <th>property</th>\n",
       "      <th>age_in_years</th>\n",
       "      <th>other_installment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>number_of_existing_credits_at_this_bank</th>\n",
       "      <th>job</th>\n",
       "      <th>number_of_people_being_liable_to_provide_maintenance_for</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>creditability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1169.0000</td>\n",
       "      <td>unknown/ no savings account</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>male : divorced/separated</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>NaN</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>yes, registered under the customers name</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5951.0000</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>1 &lt;= ... &lt; 4 years</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>male : divorced/separated</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no checking account</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>critical account/ other credits existing (not at this bank)</td>\n",
       "      <td>education</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4 &lt;= ... &lt; 7 years</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>male : divorced/separated</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>own</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882.0000</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male : divorced/separated</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>...</td>\n",
       "      <td>building society savings agreement/ life insurance</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for free</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>car (new)</td>\n",
       "      <td>4870.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 &lt;= ... &lt; 4 years</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>male : divorced/separated</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  status_of_existing_checking_account  duration_in_month                                               credit_history              purpose  credit_amount    savings_account_and_bonds present_employment_since  installment_rate_in_percentage_of_disposable_income    personal_status_and_sex other_debtors_or_guarantors  ...                                            property age_in_years  other_installment_plans   housing number_of_existing_credits_at_this_bank                          job number_of_people_being_liable_to_provide_maintenance_for                                 telephone foreign_worker creditability\n",
       "0                          ... < 0 DM             6.0000                                                          NaN                  NaN      1169.0000  unknown/ no savings account                      NaN                                               4.0000  male : divorced/separated                        none  ...                                         real estate      67.0000                     none       own                                     NaN  skilled employee / official                                                   1.0000  yes, registered under the customers name            yes             0\n",
       "1                                 NaN            48.0000                     existing credits paid back duly till now                  NaN      5951.0000                 ... < 100 DM       1 <= ... < 4 years                                               2.0000  male : divorced/separated                        none  ...                                         real estate          NaN                     none       own                                  1.0000  skilled employee / official                                                   1.0000                                      none            yes             1\n",
       "2                 no checking account            12.0000  critical account/ other credits existing (not at this bank)            education            NaN                          NaN       4 <= ... < 7 years                                               2.0000  male : divorced/separated                         NaN  ...                                                 NaN      49.0000                      NaN       own                                  1.0000         unskilled - resident                                                   2.0000                                       NaN            yes             0\n",
       "3                          ... < 0 DM                NaN                     existing credits paid back duly till now  furniture/equipment      7882.0000                 ... < 100 DM                      NaN                                                  NaN  male : divorced/separated                   guarantor  ...  building society savings agreement/ life insurance      45.0000                      NaN  for free                                  1.0000  skilled employee / official                                                      NaN                                      none            NaN             0\n",
       "4                          ... < 0 DM            24.0000                                                          NaN            car (new)      4870.0000                          NaN       1 <= ... < 4 years                                               3.0000  male : divorced/separated                        none  ...                               unknown / no property      53.0000                     none  for free                                     NaN                          NaN                                                   2.0000                                      none            yes             1\n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = \"creditability\"\n",
    "data = germancredit()\n",
    "data[target] = data[target].map({\"good\": 0, \"bad\": 1})\n",
    "\n",
    "# 随机替换 20% 的数据为 np.nan\n",
    "for col in data.columns.drop(target):\n",
    "    for i in range(len(data)):\n",
    "        if np.random.rand() > 0.8:\n",
    "            data[col].loc[i] = np.nan\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2024-08-16 17:34:47,039 ][ INFO ][ 3133036894.py:<module>:3 ] 训练集数据: (700, 21), 测试集数据: (300, 21)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(data, test_size=0.3, shuffle=True, stratify=data[target])\n",
    "\n",
    "logger.info(f\"训练集数据: {train.shape}, 测试集数据: {test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数据集</th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>样本总数</th>\n",
       "      <th>样本占比</th>\n",
       "      <th>坏客户数</th>\n",
       "      <th>坏客户占比</th>\n",
       "      <th>备注</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>建模样本</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>300</td>\n",
       "      <td>0.3000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>训练集</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>700</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>210</td>\n",
       "      <td>0.3000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>测试集</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>300</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>90</td>\n",
       "      <td>0.3000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    数据集        开始时间        结束时间  样本总数   样本占比  坏客户数  坏客户占比 备注\n",
       "0  建模样本  2022-01-01  2023-01-31  1000 1.0000   300 0.3000   \n",
       "1   训练集  2022-01-01  2023-12-31   700 0.7000   210 0.3000   \n",
       "2   测试集  2022-01-01  2023-12-31   300 0.3000    90 0.3000   "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模拟实际场景中的数据， date 为数据集中的日期，为 datetime 类型，实际生产过程中可能是 申请时间｜放款时间｜入催时间｜流失时间 等\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df[\"date\"] = pd.date_range(start=\"2021-01-01\", end=\"2021-06-30\", freq=\"5H\")\n",
    "df[target] = np.random.randint(0, 2, len(df))\n",
    "\n",
    "total_count = len(data)\n",
    "dataset_summary = pd.DataFrame(\n",
    "    [\n",
    "        [\"建模样本\", \"2022-01-01\", \"2023-01-31\", len(data), len(data) / total_count, data[target].sum(), data[target].sum() / len(data), \"\"],\n",
    "        [\"训练集\", \"2022-01-01\", \"2023-12-31\", len(train), len(train) / total_count, train[target].sum(), train[target].sum() / len(train), \"\"],\n",
    "        [\"测试集\", \"2022-01-01\", \"2023-12-31\", len(test), len(test) / total_count, test[target].sum(), test[target].sum() / len(test), \"\"],\n",
    "    ],\n",
    "    columns=[\"数据集\", \"开始时间\", \"结束时间\", \"样本总数\", \"样本占比\", \"坏客户数\", \"坏客户占比\", \"备注\"],\n",
    ")\n",
    "\n",
    "dataset_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(df, date=\"date\", target=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = FeatureSelection(target=target, engine=\"toad\", identical=0.95, empty=0.95, iv=0.02, corr=0.6)\n",
    "select.fit(train)\n",
    "\n",
    "train_select = select.transform(train)\n",
    "test_select = select.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "combiner = Combiner(target=target, min_bin_size=0.2, empty_separate=True)\n",
    "\n",
    "combiner.fit(train_select)\n",
    "\n",
    "train_bins = combiner.transform(train_select)\n",
    "test_bins = combiner.transform(test_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>指标名称</th>\n",
       "      <th>指标含义</th>\n",
       "      <th>分箱</th>\n",
       "      <th>样本总数</th>\n",
       "      <th>样本占比</th>\n",
       "      <th>好样本数</th>\n",
       "      <th>好样本占比</th>\n",
       "      <th>坏样本数</th>\n",
       "      <th>坏样本占比</th>\n",
       "      <th>坏样本率</th>\n",
       "      <th>分档WOE值</th>\n",
       "      <th>分档IV值</th>\n",
       "      <th>指标IV值</th>\n",
       "      <th>LIFT值</th>\n",
       "      <th>坏账改善</th>\n",
       "      <th>累积LIFT值</th>\n",
       "      <th>累积坏账改善</th>\n",
       "      <th>累积好样本数</th>\n",
       "      <th>累积坏样本数</th>\n",
       "      <th>分档KS值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>信用额度</td>\n",
       "      <td>[负无穷 , 4000.0)</td>\n",
       "      <td>431</td>\n",
       "      <td>0.6157</td>\n",
       "      <td>325</td>\n",
       "      <td>0.6633</td>\n",
       "      <td>106</td>\n",
       "      <td>0.5048</td>\n",
       "      <td>0.2459</td>\n",
       "      <td>0.2731</td>\n",
       "      <td>0.0433</td>\n",
       "      <td>0.1158</td>\n",
       "      <td>0.8198</td>\n",
       "      <td>-0.2887</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>490</td>\n",
       "      <td>210</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>信用额度</td>\n",
       "      <td>[4000.0 , 正无穷)</td>\n",
       "      <td>139</td>\n",
       "      <td>0.1986</td>\n",
       "      <td>80</td>\n",
       "      <td>0.1633</td>\n",
       "      <td>59</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>-0.5428</td>\n",
       "      <td>0.0639</td>\n",
       "      <td>0.1158</td>\n",
       "      <td>1.4149</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>1.2887</td>\n",
       "      <td>0.1802</td>\n",
       "      <td>165</td>\n",
       "      <td>104</td>\n",
       "      <td>0.1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>信用额度</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>130</td>\n",
       "      <td>0.1857</td>\n",
       "      <td>85</td>\n",
       "      <td>0.1735</td>\n",
       "      <td>45</td>\n",
       "      <td>0.2143</td>\n",
       "      <td>0.3462</td>\n",
       "      <td>-0.2113</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.1158</td>\n",
       "      <td>1.1538</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>1.1538</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            指标名称  指标含义              分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善  累积好样本数  累积坏样本数  分档KS值\n",
       "0  credit_amount  信用额度  [负无穷 , 4000.0)   431 0.6157   325 0.6633   106 0.5048 0.2459  0.2731 0.0433 0.1158 0.8198 -0.2887   1.0000  0.0000     490     210 0.0000\n",
       "1  credit_amount  信用额度  [4000.0 , 正无穷)   139 0.1986    80 0.1633    59 0.2810 0.4245 -0.5428 0.0639 0.1158 1.4149  0.1028   1.2887  0.1802     165     104 0.1585\n",
       "2  credit_amount  信用额度             缺失值   130 0.1857    85 0.1735    45 0.2143 0.3462 -0.2113 0.0086 0.1158 1.1538  0.0351   1.1538  0.0351      85      45 0.0408"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combiner.bin_plot(train_select, \"credit_amount\", result=True, desc=\"信用额度\", rule=[4000.0, np.nan])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = WOETransformer(target=target)\n",
    "transform.fit(train_bins)\n",
    "\n",
    "train_woe = transform.transform(train_bins)\n",
    "test_woe = transform.transform(test_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化逐步回归特征筛选器\n",
    "stepwise = StepwiseSelection(target=target)\n",
    "# 训练\n",
    "stepwise.fit(train_woe)\n",
    "# 应用逐步回归特征筛选器\n",
    "train_woe_stepwise = stepwise.transform(train_woe)\n",
    "test_woe_stepwise = stepwise.transform(test_woe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 逻辑回归模型构建\n",
    "logistic = ITLubberLogisticRegression(target=target)\n",
    "# 训练\n",
    "logistic.fit(train_woe_stepwise)\n",
    "# 预测数据集样本违约概率\n",
    "y_pred_train = logistic.predict_proba(train_woe_stepwise.drop(columns=target))[:, 1]\n",
    "y_pred_test = logistic.predict_proba(test_woe_stepwise.drop(columns=target))[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Describe</th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[ 0.025</th>\n",
       "      <th>0.975 ]</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>截距项</td>\n",
       "      <td>-0.8423</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>-8.9407</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1.0269</td>\n",
       "      <td>-0.6576</td>\n",
       "      <td>1.0499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>现有支票账户的状态</td>\n",
       "      <td>0.9018</td>\n",
       "      <td>0.1295</td>\n",
       "      <td>6.9653</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6480</td>\n",
       "      <td>1.1555</td>\n",
       "      <td>1.0828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purpose</td>\n",
       "      <td>目的</td>\n",
       "      <td>1.0039</td>\n",
       "      <td>0.3082</td>\n",
       "      <td>3.2577</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.3999</td>\n",
       "      <td>1.6079</td>\n",
       "      <td>1.0108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>信用额度</td>\n",
       "      <td>1.1053</td>\n",
       "      <td>0.2571</td>\n",
       "      <td>4.2988</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6014</td>\n",
       "      <td>1.6093</td>\n",
       "      <td>1.0346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td></td>\n",
       "      <td>0.6072</td>\n",
       "      <td>0.2516</td>\n",
       "      <td>2.4130</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>1.1004</td>\n",
       "      <td>1.0635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>present_employment_since</td>\n",
       "      <td>现居住地至今</td>\n",
       "      <td>0.6739</td>\n",
       "      <td>0.3667</td>\n",
       "      <td>1.8379</td>\n",
       "      <td>0.0661</td>\n",
       "      <td>-0.0448</td>\n",
       "      <td>1.3926</td>\n",
       "      <td>1.0612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>分期付款率占可支配收入的百分比</td>\n",
       "      <td>1.2856</td>\n",
       "      <td>0.4382</td>\n",
       "      <td>2.9338</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.4267</td>\n",
       "      <td>2.1444</td>\n",
       "      <td>1.0178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>personal_status_and_sex</td>\n",
       "      <td>个人地位和性别</td>\n",
       "      <td>0.8099</td>\n",
       "      <td>0.5258</td>\n",
       "      <td>1.5404</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>-0.2206</td>\n",
       "      <td>1.8405</td>\n",
       "      <td>1.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>property</td>\n",
       "      <td></td>\n",
       "      <td>0.8269</td>\n",
       "      <td>0.3841</td>\n",
       "      <td>2.1527</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>1.5797</td>\n",
       "      <td>1.0279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>年龄</td>\n",
       "      <td>0.9057</td>\n",
       "      <td>0.2749</td>\n",
       "      <td>3.2946</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.3669</td>\n",
       "      <td>1.4445</td>\n",
       "      <td>1.0459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Features         Describe   Coef.  Std.Err       z  P>|z|  [ 0.025  0.975 ]    VIF\n",
       "0                                                const              截距项 -0.8423   0.0942 -8.9407 0.0000  -1.0269  -0.6576 1.0499\n",
       "1                  status_of_existing_checking_account        现有支票账户的状态  0.9018   0.1295  6.9653 0.0000   0.6480   1.1555 1.0828\n",
       "2                                              purpose               目的  1.0039   0.3082  3.2577 0.0011   0.3999   1.6079 1.0108\n",
       "3                                        credit_amount             信用额度  1.1053   0.2571  4.2988 0.0000   0.6014   1.6093 1.0346\n",
       "4                            savings_account_and_bonds                   0.6072   0.2516  2.4130 0.0158   0.1140   1.1004 1.0635\n",
       "5                             present_employment_since           现居住地至今  0.6739   0.3667  1.8379 0.0661  -0.0448   1.3926 1.0612\n",
       "6  installment_rate_in_percentage_of_disposable_income  分期付款率占可支配收入的百分比  1.2856   0.4382  2.9338 0.0033   0.4267   2.1444 1.0178\n",
       "7                              personal_status_and_sex          个人地位和性别  0.8099   0.5258  1.5404 0.1235  -0.2206   1.8405 1.0106\n",
       "8                                             property                   0.8269   0.3841  2.1527 0.0313   0.0740   1.5797 1.0279\n",
       "9                                         age_in_years               年龄  0.9057   0.2749  3.2946 0.0010   0.3669   1.4445 1.0459"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据字典或特征描述信息\n",
    "feature_map = {\n",
    "    \"const\": \"截距项\",\n",
    "    \"status_of_existing_checking_account\": \"现有支票账户的状态\",\n",
    "    \"credit_history\": \"信用记录\",\n",
    "    \"purpose\": \"目的\",\n",
    "    \"credit_amount\": \"信用额度\",\n",
    "    \"present_employment_since\": \"现居住地至今\",\n",
    "    \"installment_rate_in_percentage_of_disposable_income\": \"分期付款率占可支配收入的百分比\",\n",
    "    \"personal_status_and_sex\": \"个人地位和性别\",\n",
    "    \"age_in_years\": \"年龄\",\n",
    "    \"housing\": \"住房情况\",\n",
    "}\n",
    "# summary 仅支持输出简单的统计信息，使用 summary2 可以输出有特征描述的统计信息表\n",
    "logistic.summary2(feature_map=feature_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic.plot_weights(figsize=(10, 6), save=\"model_report/sp_lr_weight.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_rate = train[target].mean()\n",
    "# 逻辑回归模型转评分卡\n",
    "card = ScoreCard(target=target, combiner=combiner, transer=transform, pretrain_lr=logistic, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\n",
    "# 训练\n",
    "card.fit(train_woe_stepwise)\n",
    "\n",
    "# 预测\n",
    "train[\"score\"] = card.predict(train)\n",
    "test[\"score\"] = card.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>指标名称</th>\n",
       "      <th>指标含义</th>\n",
       "      <th>分箱</th>\n",
       "      <th>样本总数</th>\n",
       "      <th>样本占比</th>\n",
       "      <th>好样本数</th>\n",
       "      <th>好样本占比</th>\n",
       "      <th>坏样本数</th>\n",
       "      <th>坏样本占比</th>\n",
       "      <th>坏样本率</th>\n",
       "      <th>分档WOE值</th>\n",
       "      <th>分档IV值</th>\n",
       "      <th>指标IV值</th>\n",
       "      <th>LIFT值</th>\n",
       "      <th>坏账改善</th>\n",
       "      <th>累积LIFT值</th>\n",
       "      <th>累积坏账改善</th>\n",
       "      <th>累积好样本数</th>\n",
       "      <th>累积坏样本数</th>\n",
       "      <th>分档KS值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>score</td>\n",
       "      <td>训练集模型评分</td>\n",
       "      <td>[负无穷 , 40)</td>\n",
       "      <td>111</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>41</td>\n",
       "      <td>0.0837</td>\n",
       "      <td>70</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.6306</td>\n",
       "      <td>-1.3822</td>\n",
       "      <td>0.3451</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>2.1021</td>\n",
       "      <td>0.2077</td>\n",
       "      <td>2.1021</td>\n",
       "      <td>0.2077</td>\n",
       "      <td>41</td>\n",
       "      <td>70</td>\n",
       "      <td>0.2497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>score</td>\n",
       "      <td>训练集模型评分</td>\n",
       "      <td>[40 , 60)</td>\n",
       "      <td>301</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>195</td>\n",
       "      <td>0.3980</td>\n",
       "      <td>106</td>\n",
       "      <td>0.5048</td>\n",
       "      <td>0.3522</td>\n",
       "      <td>-0.2377</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>0.1312</td>\n",
       "      <td>1.4239</td>\n",
       "      <td>0.6065</td>\n",
       "      <td>236</td>\n",
       "      <td>176</td>\n",
       "      <td>0.3565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>score</td>\n",
       "      <td>训练集模型评分</td>\n",
       "      <td>[60 , 80)</td>\n",
       "      <td>227</td>\n",
       "      <td>0.3243</td>\n",
       "      <td>196</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.1476</td>\n",
       "      <td>0.1366</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>0.2516</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>0.4552</td>\n",
       "      <td>-0.2615</td>\n",
       "      <td>1.0798</td>\n",
       "      <td>0.8361</td>\n",
       "      <td>432</td>\n",
       "      <td>207</td>\n",
       "      <td>0.1041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>score</td>\n",
       "      <td>训练集模型评分</td>\n",
       "      <td>[80 , 正无穷)</td>\n",
       "      <td>61</td>\n",
       "      <td>0.0871</td>\n",
       "      <td>58</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>2.1145</td>\n",
       "      <td>0.2201</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>0.1639</td>\n",
       "      <td>-0.0798</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>490</td>\n",
       "      <td>210</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    指标名称     指标含义          分箱  样本总数   样本占比  好样本数  好样本占比  坏样本数  坏样本占比   坏样本率  分档WOE值  分档IV值  指标IV值  LIFT值    坏账改善  累积LIFT值  累积坏账改善  累积好样本数  累积坏样本数  分档KS值\n",
       "0  score  训练集模型评分  [负无穷 , 40)   111 0.1586    41 0.0837    70 0.3333 0.6306 -1.3822 0.3451 0.8421 2.1021  0.2077   2.1021  0.2077      41      70 0.2497\n",
       "1  score  训练集模型评分   [40 , 60)   301 0.4300   195 0.3980   106 0.5048 0.3522 -0.2377 0.0254 0.8421 1.1739  0.1312   1.4239  0.6065     236     176 0.3565\n",
       "2  score  训练集模型评分   [60 , 80)   227 0.3243   196 0.4000    31 0.1476 0.1366  0.9968 0.2516 0.8421 0.4552 -0.2615   1.0798  0.8361     432     207 0.1041\n",
       "3  score  训练集模型评分  [80 , 正无穷)    61 0.0871    58 0.1184     3 0.0143 0.0492  2.1145 0.2201 0.8421 0.1639 -0.0798   1.0000  0.0000     490     210 0.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练集评分排序性\n",
    "score_clip = card.score_clip(train[\"score\"], clip=20)\n",
    "score_table_train = feature_bin_stats(train, \"score\", desc=\"训练集模型评分\", target=target, rules=score_clip)\n",
    "bin_plot(score_table_train, desc=\"训练集模型评分\", figsize=(10, 6), anchor=0.935, save=\"model_report/train_score_bins.png\")\n",
    "\n",
    "display(score_table_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"savings_account_and_bonds\" in combiner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'unknown/ no savings account,... >= 1000 DM,500 <= ... < 1000 DM',\n",
       " 1: '... < 100 DM,100 <= ... < 500 DM',\n",
       " -1: '缺失值'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_bins(np.array(combiner[\"savings_account_and_bonds\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 查看某个特征的 PSI\n",
    "# score_clip = card.score_clip(train[\"score\"], clip=10)\n",
    "# score_table_train = feature_bin_stats(train, \"score\", desc=\"训练集模型评分\", target=target, rules=score_clip)\n",
    "# score_table_test = feature_bin_stats(test, \"score\", desc=\"测试集模型评分\", target=target, rules=score_clip)\n",
    "# train_test_score_psi = psi_plot(score_table_train, score_table_test, labels=[\"训练数据集\", \"测试数据集\"], save=\"model_report/train_test_psiplot.png\", result=True)\n",
    "\n",
    "# # 查看某个入模特征的 CSI\n",
    "# for col in card._feature_names:\n",
    "#     feature_table_train = feature_bin_stats(train, col, target=target, desc=\"训练集分布\", combiner=combiner)\n",
    "#     feature_table_test = feature_bin_stats(test, col, target=target, desc=\"测试集分布\", combiner=combiner)\n",
    "#     train_test_csi_table = csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/csi_{col}.png\")\n",
    "#     if col == \"savings_account_and_bonds\": break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in card._feature_names:\n",
    "#     print(col)\n",
    "#     feature_table = feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "#     _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", figsize=(8, 4), anchor=0.9)\n",
    "    \n",
    "#     display(feature_table)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bins': array([35., nan]),\n",
       " 'woes': array([ 0.35631058, -0.40546511, -0.08982696]),\n",
       " 'weight': 0.9057217143033184,\n",
       " 'scores': array([ 0.89168832, 10.84566059,  6.7212793 ])}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card[\"age_in_years\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_pickle(card, \"model_report/scorecard.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scorecard_pipeline = card.scorecard2pmml(pmml=\"model_report/scorecard.pmml\", debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.DataFrame({\"pred\": scorecard_pipeline.predict(train), \"true\": train[\"score\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 105 candidates, totalling 315 fits\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\nAll the 315 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n315 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\", line 422, in fit\n    fit_params_steps = self._check_fit_params(**fit_params)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\", line 341, in _check_fit_params\n    raise ValueError(\nValueError: Pipeline.fit does not accept the error_score parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 24\u001b[0m\n\u001b[1;32m     14\u001b[0m params_grid \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcombiner__max_n_bins\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;241m3\u001b[39m],\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogistic__C\u001b[39m\u001b[38;5;124m\"\u001b[39m: [np\u001b[38;5;241m.\u001b[39mpower(\u001b[38;5;241m2\u001b[39m, i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5\u001b[39m)],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogistic__solver\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msag\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;66;03m# [\"liblinear\", \"sag\", \"lbfgs\", \"newton-cg\"],\u001b[39;00m\n\u001b[1;32m     21\u001b[0m }\n\u001b[1;32m     23\u001b[0m pipeline_grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(model_pipeline, params_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mroc_auc\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, return_train_score\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 24\u001b[0m \u001b[43mpipeline_grid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mraise\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(pipeline_grid_search\u001b[38;5;241m.\u001b[39mbest_params_)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:898\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    892\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[1;32m    893\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[1;32m    894\u001b[0m     )\n\u001b[1;32m    896\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[0;32m--> 898\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    900\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[1;32m    901\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[1;32m    902\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:1422\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[1;32m   1421\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1422\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:875\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m!=\u001b[39m n_candidates \u001b[38;5;241m*\u001b[39m n_splits:\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    870\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcv.split and cv.get_n_splits returned \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    871\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minconsistent results. Expected \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplits, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_splits, \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m n_candidates)\n\u001b[1;32m    873\u001b[0m     )\n\u001b[0;32m--> 875\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# For callable self.scoring, the return type is only know after\u001b[39;00m\n\u001b[1;32m    878\u001b[0m \u001b[38;5;66;03m# calling. If the return type is a dictionary, the error scores\u001b[39;00m\n\u001b[1;32m    879\u001b[0m \u001b[38;5;66;03m# can now be inserted with the correct key. The type checking\u001b[39;00m\n\u001b[1;32m    880\u001b[0m \u001b[38;5;66;03m# of out will be done in `_insert_error_scores`.\u001b[39;00m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscoring):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:414\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[0;34m(results, error_score)\u001b[0m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[1;32m    408\u001b[0m     all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    409\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    410\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    411\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    412\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    413\u001b[0m     )\n\u001b[0;32m--> 414\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    417\u001b[0m     some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    419\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    424\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: \nAll the 315 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n315 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\", line 422, in fit\n    fit_params_steps = self._check_fit_params(**fit_params)\n  File \"/Users/lubberit/anaconda3/lib/python3.8/site-packages/sklearn/pipeline.py\", line 341, in _check_fit_params\n    raise ValueError(\nValueError: Pipeline.fit does not accept the error_score parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.\n"
     ]
    }
   ],
   "source": [
    "# 导入超参数搜索方法\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 构建 pipeline\n",
    "model_pipeline = Pipeline([\n",
    "    (\"preprocessing\", FeatureSelection(target=target, engine=\"toad\")),\n",
    "    (\"combiner\", Combiner(target=target, min_bin_size=0.2, adj_rules=None)),\n",
    "    (\"transform\", WOETransformer(target=target)),\n",
    "    (\"processing_select\", FeatureSelection(target=target, engine=\"toad\")),\n",
    "    (\"stepwise\", StepwiseSelection(target=target)),\n",
    "    (\"logistic\", ITLubberLogisticRegression(target=target)),\n",
    "])\n",
    "\n",
    "params_grid = {\n",
    "    \"combiner__max_n_bins\": [3],\n",
    "    \"logistic__C\": [np.power(2, i) for i in range(5)],\n",
    "    \"logistic__penalty\": [\"l2\"],\n",
    "    \"logistic__class_weight\": [None, \"balanced\"] + [{1: i / 10.0, 0: 1 - i / 10.0} for i in range(1, 10, 2)],\n",
    "    \"logistic__max_iter\": [10, 50, 100],\n",
    "    \"logistic__solver\": [\"sag\"], # [\"liblinear\", \"sag\", \"lbfgs\", \"newton-cg\"],\n",
    "}\n",
    "\n",
    "pipeline_grid_search = GridSearchCV(model_pipeline, params_grid, cv=3, scoring='roc_auc', verbose=1, n_jobs=-1, return_train_score=True)\n",
    "pipeline_grid_search.fit(train, train[target], error_score='raise')\n",
    "\n",
    "print(pipeline_grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] filtering variables ...\n",
      "|    | 变量名称                                            | 变量分箱                                                                                                                             |   对应分数 |\n",
      "|---:|:----------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|-----------:|\n",
      "|  0 | property                                            | real estate                                                                                                                          |    11.2541 |\n",
      "|  1 | property                                            | unknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值 |     4.0428 |\n",
      "|  2 | present_employment_since                            | ... >= 7 years,4 <= ... < 7 years                                                                                                    |     9.4896 |\n",
      "|  3 | present_employment_since                            | 1 <= ... < 4 years,... < 1 year,unemployed,缺失值                                                                                    |     3.8957 |\n",
      "|  4 | credit_amount                                       | [负无穷 , 2145.0)                                                                                                                    |     8.0057 |\n",
      "|  5 | credit_amount                                       | [2145.0 , 3804.0)                                                                                                                    |    14.2751 |\n",
      "|  6 | credit_amount                                       | [3804.0 , 正无穷)                                                                                                                    |    -2.6347 |\n",
      "|  7 | credit_amount                                       | 缺失值                                                                                                                               |     2.1778 |\n",
      "|  8 | installment_rate_in_percentage_of_disposable_income | [负无穷 , 4.0)                                                                                                                       |     7.7878 |\n",
      "|  9 | installment_rate_in_percentage_of_disposable_income | [4.0 , 正无穷)                                                                                                                       |     0.9877 |\n",
      "| 10 | installment_rate_in_percentage_of_disposable_income | 缺失值                                                                                                                               |    10.7462 |\n",
      "| 11 | age_in_years                                        | [负无穷 , 35.0)                                                                                                                      |     0.8917 |\n",
      "| 12 | age_in_years                                        | [35.0 , 正无穷)                                                                                                                      |    10.8457 |\n",
      "| 13 | age_in_years                                        | 缺失值                                                                                                                               |     6.7213 |\n",
      "| 14 | savings_account_and_bonds                           | ... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM                                                                      |     5.9834 |\n",
      "| 15 | savings_account_and_bonds                           | 100 <= ... < 500 DM,... < 100 DM                                                                                                     |    12.1585 |\n",
      "| 16 | savings_account_and_bonds                           | 缺失值                                                                                                                               |     3.2466 |\n",
      "| 17 | purpose                                             | radio/television,car (used)                                                                                                          |    12.8514 |\n",
      "| 18 | purpose                                             | education,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances                                |     2.7818 |\n",
      "| 19 | personal_status_and_sex                             | 缺失值,female : divorced/separated/married                                                                                           |     7.8997 |\n",
      "| 20 | personal_status_and_sex                             | male : married/widowed,male : single,male : divorced/separated                                                                       |     3.7463 |\n",
      "| 21 | status_of_existing_checking_account                 | no checking account                                                                                                                  |    22.4653 |\n",
      "| 22 | status_of_existing_checking_account                 | ... >= 200 DM / salary assignments for at least 1 year,缺失值                                                                        |     6.6694 |\n",
      "| 23 | status_of_existing_checking_account                 | 0 <= ... < 200 DM                                                                                                                    |     0.0181 |\n",
      "| 24 | status_of_existing_checking_account                 | ... < 0 DM                                                                                                                           |    -4.8558 |\n"
     ]
    }
   ],
   "source": [
    "# 构建 pipeline\n",
    "model_pipeline = Pipeline([\n",
    "    (\"preprocessing\", FeatureSelection(target=target, engine=\"scorecardpy\")),\n",
    "    (\"combiner\", Combiner(target=target, min_bin_size=0.2)),\n",
    "    (\"transform\", WOETransformer(target=target)),\n",
    "    (\"processing_select\", FeatureSelection(target=target, engine=\"toad\")),\n",
    "    (\"stepwise\", StepwiseSelection(target=target)),\n",
    "    (\"logistic\", ITLubberLogisticRegression(target=target)),\n",
    "])\n",
    "# 训练 pipeline\n",
    "model_pipeline.fit(train)\n",
    "# 转换评分卡\n",
    "card = ScoreCard(target=target, pipeline=model_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\n",
    "card.fit(model_pipeline[:-1].transform(train))\n",
    "print(card.scorecard_points().to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变量名称</th>\n",
       "      <th>变量分箱</th>\n",
       "      <th>对应分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>property</td>\n",
       "      <td>real estate</td>\n",
       "      <td>11.2541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>property</td>\n",
       "      <td>unknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值</td>\n",
       "      <td>4.0428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>present_employment_since</td>\n",
       "      <td>... &gt;= 7 years,4 &lt;= ... &lt; 7 years</td>\n",
       "      <td>9.4896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>present_employment_since</td>\n",
       "      <td>1 &lt;= ... &lt; 4 years,... &lt; 1 year,unemployed,缺失值</td>\n",
       "      <td>3.8957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[负无穷 , 2145.0)</td>\n",
       "      <td>8.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[2145.0 , 3804.0)</td>\n",
       "      <td>14.2751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[3804.0 , 正无穷)</td>\n",
       "      <td>-2.6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>2.1778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>[负无穷 , 4.0)</td>\n",
       "      <td>7.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>[4.0 , 正无穷)</td>\n",
       "      <td>0.9877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>10.7462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>[负无穷 , 35.0)</td>\n",
       "      <td>0.8917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>[35.0 , 正无穷)</td>\n",
       "      <td>10.8457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>6.7213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>... &gt;= 1000 DM,unknown/ no savings account,500 &lt;= ... &lt; 1000 DM</td>\n",
       "      <td>5.9834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>100 &lt;= ... &lt; 500 DM,... &lt; 100 DM</td>\n",
       "      <td>12.1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>3.2466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>purpose</td>\n",
       "      <td>radio/television,car (used)</td>\n",
       "      <td>12.8514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>purpose</td>\n",
       "      <td>education,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances</td>\n",
       "      <td>2.7818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>personal_status_and_sex</td>\n",
       "      <td>缺失值,female : divorced/separated/married</td>\n",
       "      <td>7.8997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>personal_status_and_sex</td>\n",
       "      <td>male : married/widowed,male : single,male : divorced/separated</td>\n",
       "      <td>3.7463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>no checking account</td>\n",
       "      <td>22.4653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>... &gt;= 200 DM / salary assignments for at least 1 year,缺失值</td>\n",
       "      <td>6.6694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>0 &lt;= ... &lt; 200 DM</td>\n",
       "      <td>0.0181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>-4.8558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   变量名称                                                                                                                               变量分箱    对应分数\n",
       "0                                              property                                                                                                                        real estate 11.2541\n",
       "1                                              property  unknown / no property,building society savings agreement/ life insurance,car or other, not in attribute Savings account/bonds,缺失值  4.0428\n",
       "2                              present_employment_since                                                                                                  ... >= 7 years,4 <= ... < 7 years  9.4896\n",
       "3                              present_employment_since                                                                                     1 <= ... < 4 years,... < 1 year,unemployed,缺失值  3.8957\n",
       "4                                         credit_amount                                                                                                                     [负无穷 , 2145.0)  8.0057\n",
       "5                                         credit_amount                                                                                                                  [2145.0 , 3804.0) 14.2751\n",
       "6                                         credit_amount                                                                                                                     [3804.0 , 正无穷) -2.6347\n",
       "7                                         credit_amount                                                                                                                                缺失值  2.1778\n",
       "8   installment_rate_in_percentage_of_disposable_income                                                                                                                        [负无穷 , 4.0)  7.7878\n",
       "9   installment_rate_in_percentage_of_disposable_income                                                                                                                        [4.0 , 正无穷)  0.9877\n",
       "10  installment_rate_in_percentage_of_disposable_income                                                                                                                                缺失值 10.7462\n",
       "11                                         age_in_years                                                                                                                       [负无穷 , 35.0)  0.8917\n",
       "12                                         age_in_years                                                                                                                       [35.0 , 正无穷) 10.8457\n",
       "13                                         age_in_years                                                                                                                                缺失值  6.7213\n",
       "14                            savings_account_and_bonds                                                                    ... >= 1000 DM,unknown/ no savings account,500 <= ... < 1000 DM  5.9834\n",
       "15                            savings_account_and_bonds                                                                                                   100 <= ... < 500 DM,... < 100 DM 12.1585\n",
       "16                            savings_account_and_bonds                                                                                                                                缺失值  3.2466\n",
       "17                                              purpose                                                                                                        radio/television,car (used) 12.8514\n",
       "18                                              purpose                                 education,retraining,others,business,furniture/equipment,缺失值,repairs,car (new),domestic appliances  2.7818\n",
       "19                              personal_status_and_sex                                                                                            缺失值,female : divorced/separated/married  7.8997\n",
       "20                              personal_status_and_sex                                                                     male : married/widowed,male : single,male : divorced/separated  3.7463\n",
       "21                  status_of_existing_checking_account                                                                                                                no checking account 22.4653\n",
       "22                  status_of_existing_checking_account                                                                         ... >= 200 DM / salary assignments for at least 1 year,缺失值  6.6694\n",
       "23                  status_of_existing_checking_account                                                                                                                  0 <= ... < 200 DM  0.0181\n",
       "24                  status_of_existing_checking_account                                                                                                                         ... < 0 DM -4.8558"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card.scorecard_points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变量名称</th>\n",
       "      <th>变量分箱</th>\n",
       "      <th>对应分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purpose</td>\n",
       "      <td>radio/television,car (used)</td>\n",
       "      <td>12.8514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purpose</td>\n",
       "      <td>business,furniture/equipment,others,education,domestic appliances,retraining,car (new),缺失值,repairs</td>\n",
       "      <td>2.7818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>[负无穷 , 4.0)</td>\n",
       "      <td>7.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>[4.0 , 正无穷)</td>\n",
       "      <td>0.9877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>installment_rate_in_percentage_of_disposable_income</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>10.7462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>500 &lt;= ... &lt; 1000 DM,unknown/ no savings account,... &gt;= 1000 DM</td>\n",
       "      <td>5.9834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>... &lt; 100 DM,100 &lt;= ... &lt; 500 DM</td>\n",
       "      <td>12.1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>savings_account_and_bonds</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>3.2466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>present_employment_since</td>\n",
       "      <td>4 &lt;= ... &lt; 7 years,... &gt;= 7 years</td>\n",
       "      <td>9.4896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>present_employment_since</td>\n",
       "      <td>缺失值,unemployed,1 &lt;= ... &lt; 4 years,... &lt; 1 year</td>\n",
       "      <td>3.8957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>[负无穷 , 35.0)</td>\n",
       "      <td>0.8917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>[35.0 , 正无穷)</td>\n",
       "      <td>10.8457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age_in_years</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>6.7213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>property</td>\n",
       "      <td>real estate</td>\n",
       "      <td>11.2541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>property</td>\n",
       "      <td>缺失值,building society savings agreement/ life insurance,unknown / no property,car or other, not in attribute Savings account/bonds</td>\n",
       "      <td>4.0428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>personal_status_and_sex</td>\n",
       "      <td>缺失值,female : divorced/separated/married</td>\n",
       "      <td>7.8997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>personal_status_and_sex</td>\n",
       "      <td>male : single,male : married/widowed,male : divorced/separated</td>\n",
       "      <td>3.7463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[负无穷 , 2145.0)</td>\n",
       "      <td>8.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[2145.0 , 3804.0)</td>\n",
       "      <td>14.2751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>[3804.0 , 正无穷)</td>\n",
       "      <td>-2.6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>credit_amount</td>\n",
       "      <td>缺失值</td>\n",
       "      <td>2.1778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>no checking account</td>\n",
       "      <td>22.4653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>缺失值,... &gt;= 200 DM / salary assignments for at least 1 year</td>\n",
       "      <td>6.6694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>0 &lt;= ... &lt; 200 DM</td>\n",
       "      <td>0.0181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>status_of_existing_checking_account</td>\n",
       "      <td>... &lt; 0 DM</td>\n",
       "      <td>-4.8558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   变量名称                                                                                                                               变量分箱    对应分数\n",
       "0                                               purpose                                                                                                        radio/television,car (used) 12.8514\n",
       "1                                               purpose                                 business,furniture/equipment,others,education,domestic appliances,retraining,car (new),缺失值,repairs  2.7818\n",
       "2   installment_rate_in_percentage_of_disposable_income                                                                                                                        [负无穷 , 4.0)  7.7878\n",
       "3   installment_rate_in_percentage_of_disposable_income                                                                                                                        [4.0 , 正无穷)  0.9877\n",
       "4   installment_rate_in_percentage_of_disposable_income                                                                                                                                缺失值 10.7462\n",
       "5                             savings_account_and_bonds                                                                    500 <= ... < 1000 DM,unknown/ no savings account,... >= 1000 DM  5.9834\n",
       "6                             savings_account_and_bonds                                                                                                   ... < 100 DM,100 <= ... < 500 DM 12.1585\n",
       "7                             savings_account_and_bonds                                                                                                                                缺失值  3.2466\n",
       "8                              present_employment_since                                                                                                  4 <= ... < 7 years,... >= 7 years  9.4896\n",
       "9                              present_employment_since                                                                                     缺失值,unemployed,1 <= ... < 4 years,... < 1 year  3.8957\n",
       "10                                         age_in_years                                                                                                                       [负无穷 , 35.0)  0.8917\n",
       "11                                         age_in_years                                                                                                                       [35.0 , 正无穷) 10.8457\n",
       "12                                         age_in_years                                                                                                                                缺失值  6.7213\n",
       "13                                             property                                                                                                                        real estate 11.2541\n",
       "14                                             property  缺失值,building society savings agreement/ life insurance,unknown / no property,car or other, not in attribute Savings account/bonds  4.0428\n",
       "15                              personal_status_and_sex                                                                                            缺失值,female : divorced/separated/married  7.8997\n",
       "16                              personal_status_and_sex                                                                     male : single,male : married/widowed,male : divorced/separated  3.7463\n",
       "17                                        credit_amount                                                                                                                     [负无穷 , 2145.0)  8.0057\n",
       "18                                        credit_amount                                                                                                                  [2145.0 , 3804.0) 14.2751\n",
       "19                                        credit_amount                                                                                                                     [3804.0 , 正无穷) -2.6347\n",
       "20                                        credit_amount                                                                                                                                缺失值  2.1778\n",
       "21                  status_of_existing_checking_account                                                                                                                no checking account 22.4653\n",
       "22                  status_of_existing_checking_account                                                                         缺失值,... >= 200 DM / salary assignments for at least 1 year  6.6694\n",
       "23                  status_of_existing_checking_account                                                                                                                  0 <= ... < 200 DM  0.0181\n",
       "24                  status_of_existing_checking_account                                                                                                                         ... < 0 DM -4.8558"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_rate = train[target].mean()\n",
    "# 逻辑回归模型转评分卡\n",
    "card = ScoreCard(target=target, pipeline=mdoel_pipeline, base_score=50, base_odds=(1 - bad_rate) / bad_rate, pdo=10)\n",
    "# 训练\n",
    "card.fit(mdoel_pipeline[:-1].transform(train))\n",
    "card.scorecard_points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据字段[可选]\n",
    "feature_describe = pd.DataFrame([\n",
    "    [\"status_account\", \"支票账户状态\"], [\"duration\", \"借款周期\"], [\"credit_histor\", \"历史信用\"], [\"purpose\", \"借款目的\"], [\"amount\", \"信用额度\"], [\"svaing_account\", \"储蓄账户状态\"], [\"present_emp\", \"当前就业状态\"], [\"income_rate\", \"分期付款占可支配收入百分比\"], [\"personal_status\", \"性别与婚姻状态\"], [\"other_debtors\", \"他人担保信息\"], [\"residence_info\", \"现居住地\"], [\"property\", \"财产状态\"], [\"age\", \"年龄\"], [\"inst_plans\", \"其他分期情况\"], [\"housing\", \"房产状态\"], [\"num_credits\", \"信用卡数量\"], [\"job\", \"工作状态\"], [\"dependents\", \"赡养人数\"], [\"telephone\", \"电话号码注册情况\"], [\"foreign_worke\", \"是否有海外工作经历\"],\n",
    "], columns=[\"变量名称\", \"变量含义\"])\n",
    "feature_map = dict(zip(feature_describe[\"变量名称\"], feature_describe[\"变量含义\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = sp.ExcelWriter()\n",
    "start_row, start_col = 2, 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ////////////////////////////////////// 样本说明 ///////////////////////////////////// #\n",
    "worksheet = writer.get_sheet_by_name(\"汇总信息\")\n",
    "\n",
    "# 样本总体分布情况\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"样本总体分布情况\", style=\"header\")\n",
    "end_row, end_col = sp.dataframe2excel(dataset_summary, writer, worksheet, percent_cols=[\"样本占比\", \"坏客户占比\"], start_row=end_row + 1)\n",
    "\n",
    "# 建模样本时间分布情况\n",
    "temp = sp.distribution_plot(df, date=\"date\", target=target, save=\"model_report/all_sample_time_count.png\", result=True)\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"建模样本时间分布情况\", style=\"header\")\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/all_sample_time_count.png\", (end_row, start_col), figsize=(720, 370))\n",
    "end_row, end_col = sp.dataframe2excel(temp, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\"], condition_cols=[\"坏样本率\"], start_row=end_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ////////////////////////////////////// 模型报告 ///////////////////////////////////// #\n",
    "summary = logistic.summary2(feature_map=feature_map)\n",
    "\n",
    "# 逻辑回归拟合情况\n",
    "worksheet = writer.get_sheet_by_name(\"逻辑回归拟合结果\")\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"逻辑回归拟合效果\", style=\"header\")\n",
    "end_row, end_col = sp.dataframe2excel(summary, writer, worksheet, condition_cols=[\"Coef.\"], start_row=end_row + 1)\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"训练数据集拟合报告\", style=\"header\")\n",
    "end_row, end_col = sp.dataframe2excel(logistic.report(train_woe_stepwise), writer, worksheet, percent_cols=[\"precision\", \"recall\", \"f1-score\"], start_row=end_row + 1)\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"测试数据集拟合报告\", style=\"header\")\n",
    "end_row, end_col = sp.dataframe2excel(logistic.report(test_woe_stepwise), writer, worksheet, percent_cols=[\"precision\", \"recall\", \"f1-score\"], start_row=end_row + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ////////////////////////////////////// 特征概述 ///////////////////////////////////// #\n",
    "worksheet = writer.get_sheet_by_name(\"模型变量信息\")\n",
    "\n",
    "start_row, start_col = 2, 2\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"入模变量信息\", style=\"header\")\n",
    "end_row, end_col = writer.insert_df2sheet(worksheet, feature_describe.reset_index().rename(columns={\"index\": \"序号\"}), (end_row + 1, start_col))\n",
    "\n",
    "# 变量分布情况\n",
    "import toad\n",
    "data_info = toad.detect(data[card.rules.keys()]).reset_index().rename(columns={\"index\": \"变量名称\", \"type\": \"变量类型\", \"size\": \"样本个数\", \"missing\": \"缺失值\", \"unique\": \"唯一值个数\"})\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量分布情况\", style=\"header\")\n",
    "end_row, end_col = writer.insert_df2sheet(worksheet, data_info, (end_row + 1, start_col))\n",
    "\n",
    "# 变量相关性\n",
    "data_corr = train_woe_stepwise.corr()\n",
    "logistic.corr(train_woe_stepwise, save=\"model_report/train_corr.png\", annot=False)\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量相关性\", style=\"header\")\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_corr.png\", (end_row + 1, start_col), figsize=(700, 500))\n",
    "end_row, end_col = sp.dataframe2excel(data_corr.reset_index().rename(columns={\"index\": \"\"}), writer, worksheet, color_cols=list(data_corr.columns), start_row=end_row + 1)\n",
    "\n",
    "# 变量分箱信息\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"变量分箱信息\", style=\"header\")\n",
    "\n",
    "for col in logistic.feature_names_in_:\n",
    "    feature_table = sp.feature_bin_stats(data, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "    _ = sp.bin_plot(feature_table, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", figsize=(8, 4), save=f\"model_report/bin_plots/data_{col}.png\")\n",
    "    \n",
    "    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/bin_plots/data_{col}.png\", (end_row + 1, start_col), figsize=(700, 400))\n",
    "    end_row, end_col = sp.dataframe2excel(feature_table, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\"], condition_cols=[\"坏样本率\", \"LIFT值\"], start_row=end_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ////////////////////////////////////// 评分卡说明 ///////////////////////////////////// #\n",
    "worksheet = writer.get_sheet_by_name(\"评分卡结果\")\n",
    "\n",
    "# 评分卡刻度\n",
    "scorecard_kedu = card.scorecard_scale()\n",
    "scorecard_points = card.scorecard_points(feature_map=feature_map)\n",
    "scorecard_clip = card.score_clip(train[\"score\"], clip=100)\n",
    "\n",
    "start_row, start_col = 2, 2\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"评分卡刻度\", style=\"header\")\n",
    "end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_kedu, (end_row + 1, start_col))\n",
    "\n",
    "# 评分卡对应分数\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"评分卡分数\", style=\"header\")\n",
    "end_row, end_col = writer.insert_df2sheet(worksheet, scorecard_points, (end_row + 1, start_col), merge_column=\"变量名称\")\n",
    "\n",
    "# 评分效果\n",
    "score_table_train = sp.feature_bin_stats(train, \"score\", desc=\"测试集模型评分\", target=target, rules=scorecard_clip)\n",
    "score_table_test = sp.feature_bin_stats(test, \"score\", desc=\"测试集模型评分\", target=target, rules=scorecard_clip)\n",
    "\n",
    "sp.ks_plot(train[\"score\"], train[target], title=\"Train \\tDataset\", save=\"model_report/train_ksplot.png\")\n",
    "sp.ks_plot(test[\"score\"], test[target], title=\"Test \\tDataset\", save=\"model_report/test_ksplot.png\")\n",
    "\n",
    "sp.hist_plot(train[\"score\"], train[target], save=\"model_report/train_scorehist.png\", bins=30)\n",
    "sp.hist_plot(test[\"score\"], test[target], save=\"model_report/test_scorehist.png\", bins=30)\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"训练数据集评分模型效果\", style=\"header\")\n",
    "ks_row = end_row\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_ksplot.png\", (ks_row, start_col))\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_scorehist.png\", (ks_row, end_col))\n",
    "end_row, end_col = sp.dataframe2excel(score_table_train, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"], condition_cols=[\"坏样本率\", \"LIFT值\", \"分档KS值\"], start_row=end_row + 1)\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"测试数据集评分模型效果\", style=\"header\")\n",
    "ks_row = end_row\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/test_ksplot.png\", (ks_row, start_col))\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/test_scorehist.png\", (ks_row, end_col))\n",
    "end_row, end_col = sp.dataframe2excel(score_table_test, writer, worksheet, percent_cols=[\"样本占比\", \"好样本占比\", \"坏样本占比\", \"坏样本率\", \"LIFT值\", \"累积LIFT值\", \"分档KS值\"], condition_cols=[\"坏样本率\", \"LIFT值\", \"分档KS值\"], start_row=end_row + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ////////////////////////////////////// 模型稳定性 ///////////////////////////////////// #\n",
    "worksheet = writer.get_sheet_by_name(\"模型稳定性\")\n",
    "start_row, start_col = 2, 2\n",
    "\n",
    "# 评分分布稳定性\n",
    "train_test_score_psi = sp.psi_plot(score_table_train, score_table_test, labels=[\"训练数据集\", \"测试数据集\"], save=\"model_report/train_test_psiplot.png\", result=True)\n",
    "\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (start_row, start_col), value=\"模型评分稳定性指标 (Population Stability Index, PSI): 训练数据集 vs 测试数据集\", style=\"header\")\n",
    "end_row, end_col = writer.insert_pic2sheet(worksheet, \"model_report/train_test_psiplot.png\", (end_row, start_col), figsize=(800, 400))\n",
    "end_row, end_col = sp.dataframe2excel(train_test_score_psi, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\"], condition_cols=[\"分档PSI值\"], start_row=end_row + 1)\n",
    "\n",
    "# 变量 PSI 表\n",
    "for col in card._feature_names:\n",
    "    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "    psi_table = sp.psi_plot(feature_table_train, feature_table_test, desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/psi_{col}.png\")\n",
    "    \n",
    "    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/psi_{col}.png\", (end_row, start_col), figsize=(700, 400))\n",
    "    end_row, end_col = sp.dataframe2excel(psi_table, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\", \"测试数据集% - 训练数据集%\"], condition_cols=[\"分档PSI值\"], start_row=end_row + 1)\n",
    "\n",
    "# 变量 CSI 表\n",
    "end_row, end_col = writer.insert_value2sheet(worksheet, (end_row + 2, start_col), value=\"入模变量稳定性指标 (Characteristic Stability Index, CSI): 训练数据集 vs 测试数据集\", style=\"header\")\n",
    "\n",
    "for col in card._feature_names:\n",
    "    feature_table_train = sp.feature_bin_stats(train, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "    feature_table_test = sp.feature_bin_stats(test, col, target=target, desc=feature_map.get(col, \"\") or \"逻辑回归入模变量\", combiner=combiner)\n",
    "    train_test_csi_table = sp.csi_plot(feature_table_train, feature_table_test, card[col], desc=col, result=True, plot=True, max_len=35, figsize=(10, 6), labels=[\"训练数据集\", \"测试数据集\"], save=f\"model_report/csi_{col}.png\")\n",
    "    \n",
    "    end_row, end_col = writer.insert_pic2sheet(worksheet, f\"model_report/csi_{col}.png\", (end_row, start_col), figsize=(700, 400))\n",
    "    end_row, end_col = sp.dataframe2excel(train_test_csi_table, writer, worksheet, percent_cols=[\"训练数据集样本占比\", \"训练数据集坏样本率\", \"测试数据集样本占比\", \"测试数据集坏样本率\", \"测试数据集% - 训练数据集%\"], condition_cols=[\"分档CSI值\"], start_row=end_row + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer.save(\"model_report/评分卡模型报告.xlsx\")"
   ]
  }
 ],
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